Face detection method and apparatus

ABSTRACT

Image fragments are formed in regions corresponding to circles searched from an input image. In a cascade of homogeneous classifiers, each classifier classifies input vectors corresponding to the image fragments into a face type and a non-face type. This procedure is performed on all images included in an image pyramid and the coordinates of a face detected based on the results of the procedures on all images.

TECHNICAL FIELD

The present invention relates to a digital image processing field, andmore particularly, relates to a method and apparatus for detecting aface in a digital image.

BACKGROUND ART

Digital images are used in current multimedia devices. A function amongbasic functions of the multimedia device determines the existence andlocation of a face in a digital image. The function is needed in thecase of sorting images stored in the device according to contents,processing an image region by a digital photographing and printingdevice, identifying and verifying in an access control and videosurveillance system, interacting with a person with a computer system,and others.

In order to solve the object of detecting a task in an image, there aremany techniques using a neural network, vector decomposition, a supportvector machine (SVM), and others. Under the condition in which an objectto be searched is not distinctly formalized, these approaches use atraining stage (e.g. parameter tuning) that needs a large number ofsamples for the object. The training stage in each approach performs atask for determining an object grade in an image, which requires a largeamount of computation and a high cost arises therefrom. Computationalcomplexity significantly increases when a location, size, and directionof a face in an image are determined in the training stage.

There are face detection systems described in U.S. Pat. Nos. 6,661,907and 6,816,611. The systems use color information of images. Thispeculiarity significantly restricts areas in which the method is appliedto because of requiring a color image capturing device.

In addition, there is a two-stage face detection system described inU.S. Pat. No. 6,940,545. The system is based on a probabilistic modelestimating color information related to the head of a person, forexample, hair and skin, in the first stage, and uses a Bayesianclassifier in the second stage. The Bayesian classifier processes ahypothesis and performs a final decision about the existence andlocation of a face in an image. This system may be embedded in a digitalcamera for precise estimation for image capturing parameters when a faceexists in the area to be photographed. However, this system inducesquite weak requirements on algorithm efficiency and processing speed,and thereby it is apparently inefficient in many other face detectiontasks.

Another system using a two-stage face detection algorithm is disclosedin U.S. Pat. No. 6,463,163. In the system, a two-element algorithmincluding liner and nonlinear filters is performed in the first stage.Correlation with a core of the linear filter is first calculated andthen the resulted correlation map is processed in order to extract localextremes. The first stage is completed by comparing intensitycharacteristics of regions related to the extremes with values obtainedfrom a model. Through the first stage, a set of regions where a facecould be located is obtained. At the second stage, the found regions areprocessed by a multilayer feed-forward neural network, and thereby thelist of faces found in images is obtained. However, the algorithm hasdrawbacks in that the stability of face orientation is low. Further, thecomputational speed of the multilayer neural networks is quite low, andtherefore it could be insufficient for running the algorithm inreal-time applications

These drawbacks were partly solved in U.S. Pat. No. 7,099,510. Itproposes an algorithm for effectively searching a location of a faceregion with computation considering shifting and scale adjusting. Thealgorithm is based on a cascade of simple classification procedures. Theconstruction and combinations of classifiers according to the cascaderesult in high accuracy of tasks and low running time. However, the facedetection effectiveness of all the classifiers is quite low.

As stated above, in the prior systems, high processing speed indetecting a face is needed. Further, due to errors occurred in detectinga face occur, the factors (e.g. an obstacle such as diversity of faces,spectacles, mustache, or a hat) having a big effect on the performanceof the system are not processed. In addition, structural complexity ofexternal environments, randomness of illumination, and others result inmany errors such as detecting a non-existent face in practice. Theseerrors are fatal to the performance of a biometric identificationsystem.

DISCLOSURE Technical Problem

The present invention has been made in an effort to provide a method andapparatus for consistently detecting a face from an image at a fasterspeed under various illumination conditions.

In addition, the present invention has been made in an effort to providea method and apparatus for reducing errors when incorrectly detecting aface from an image under complex environments having various structures.

TECHNICAL SOLUTION

An exemplary embodiment of the present invention provides a method fordetecting a face. The method includes: detecting a circle from an inputimage by using a Hough transform; forming image fragments of the samesize in a region of the detected circle; generating input vectors foreach of the image fragments by using each classifier of a cascade thatincludes homogeneous classifiers; and when an input vector generated bythe image fragments is classified into a face type by the classifier ofthe cascade, determining that a face is detected from the input image.

Another exemplary embodiment of the present invention provides anapparatus for detecting a face. The apparatus includes: a cascadeclassifier that is formed in a type of a cascade, includes homogeneousclassifiers, and classifies input vectors into a face type and anon-face type based on input images; a circle detector that detects acircle from the input image by using a Hough transform; and an imageanalyzer that forms image fragments of the same size within a region ofthe detected circle, wherein the image analyzer generates input vectorsfor the image fragments by using the classifiers of the cascadeclassifier, and determines that a face is detected from the input imagewhen the input vector is classified into the face type by all theclassifiers.

ADVANTAGEOUS EFFECTS

According to the exemplary embodiments of the present invention, it ispossible to correctly detect a face from an image at faster speed. Undercomplex environments having various structures or in which illuminationchanges, the number of errors of incorrectly detecting a face from animage can be reduced.

DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a configuration diagram of an apparatus for detecting aface according to an exemplary embodiment of the present invention.

FIG. 2 shows a configuration of a face detecting unit according to anexemplary embodiment of the present invention.

FIG. 3 shows a configuration of a multilevel face detector according toan exemplary embodiment of the present invention.

FIG. 4 shows a configuration of a face detector according to anexemplary embodiment of the present invention.

FIG. 5 shows an operation of a classifier according to an exemplaryembodiment of the present invention.

FIG. 6 shows a local binary pattern (LBP) procedure according to anexemplary embodiment of the present invention.

FIG. 7 shows a flowchart of a method for detecting a face according toan exemplary embodiment of the present invention.

MODE FOR INVENTION

In the following detailed description, only certain exemplaryembodiments of the present invention have been shown and described,simply by way of illustration. As those skilled in the art wouldrealize, the described embodiments may be modified in various differentways, all without departing from the spirit or scope of the presentinvention. Accordingly, the drawings and description are to be regardedas illustrative in nature and not restrictive. Like reference numeralsdesignate like elements throughout the specification.

Through the specification, in addition, unless explicitly described tothe contrary, the word “comprise” and variations such as “comprises” or“comprising” will be understood to imply the inclusion of statedelements but not the exclusion of any other elements.

Next, referring to the drawings, a method and apparatus for detecting aface according to an exemplary embodiment of the present invention willbe described.

In an exemplary embodiment of the present invention, homogeneousclassifiers of a cascade type are used for detecting a face from animage. Each classifier classifies input vectors into a face type and anon-face type, and has high precision over the previous classifier ofthe previous stage.

Particularly, in an exemplary embodiment of the present invention,circles having a predetermined radius are searched from an input imageby using a Hough transform, the regions corresponding to the searchedcircles are divided into fragments with the same size, and a set of thefragments is formed. The homogeneous classifiers of the cascade type areapplied to each fragment included in the set so that each classifierdetermines whether a corresponding fragment includes a face.

FIG. 1 shows a configuration diagram of an apparatus for detecting aface according to an exemplary embodiment of the present invention.

As shown in FIG. 1, the apparatus for detecting a face according to anexemplary embodiment of the present invention includes an imageobtaining unit 10 for obtaining an image from an object, a facedetecting unit 20 for detecting a face from the obtained image, and atraining unit 30 for constructing classifiers for face detecting.

The image obtaining unit 10 obtains an image as a source for detecting aface and may include a device such as a digital camera, an imagedatabase, or a device for selecting an image from an image sequence.

The face detecting unit 20 detects a face from the image provided fromthe image obtaining unit 10. The image in which the face detected by theface detecting unit 20 is marked is output and the coordinates of theface are output.

The training unit 30 is used in the training stage according to anexemplary embodiment of the present invention. The training unit 30performs a training procedure to train samples including face images andnon-face images, and constructs classifiers running the trainingprocedure. The training procedure is performed once before starting useof classifiers. The results of the training procedure are provided tothe face detecting unit 20, and the face detecting unit 20 detects aface from an input image with an obtained classifier.

FIG. 2 shows a configuration diagram of the face detecting unitaccording to an exemplary embodiment of the present invention.

As shown in FIG. 2, the face detecting unit 20 according to an exemplaryembodiment of the present invention includes an image pyramidconstructor 21 and a multistage face detector 22.

The image pyramid constructor 21 is provided with an image, and theimage may be provided from a video camera, am image database, a videosequence, or other digital image sources. The image pyramid constructor21 forms a collection of images in which each image is a copy of asource image scaled down by a predetermined ratio. That is, an imageprovided from the image obtaining unit 10 becomes a source image, andthen the source image is scaled down with the predetermined ratio. Eachimage of the collection is a copy of the source image. Here, thepredetermined ratio for scale adjustment may be different by imagesincluded in the collection. The collection of these images may bereferred to as “an image pyramid”, and the image pyramid is provided tothe multistage face detector 22.

The multistage face detector 22 detects a face from each image of theimage pyramid and generates results of images in which the detected faceis indicated. Also, the multistage face detector 22 generates a facecoordinate set of coordinates for the detected face.

The face detecting unit 20 may realized to include a digital processor,a source image storage, and a spare buffer, and the image pyramidconstructor 21 and the multistage face detector 22 may be embodied andoperated in a digital processor.

FIG. 3 shows a configuration of the multilevel face detector accordingto an exemplary embodiment of the present invention.

As shown in FIG. 3, the multistage face detector 22 according to anexemplary embodiment of the present invention includes a face detector221 and a face integrator 222. Basic data processed in the face detector221 is the image pyramid.

The face detector 221 detects a face from each image included in theimage pyramid, and provides the face coordinates corresponding to thedetected face to the face integrator 222.

The face integrator 222 integrates the face coordinates to be inputtedand finally calculates the coordinates of the detected face. The faceintegrator 222 performs clusterization of the coordinates of thedetected face and analyzes the parameter (e.g. a size, confidence level)of each cluster. The face integrator 222 determines which clusterrelates to a face in an input image based on a predetermined thresholdvalue. Here, a confidence level is calculated based on a sum ofconfidence values of every cluster element. The confidence values ofevery cluster element are included in an output value as the result offace detection processing.

FIG. 4 shows a configuration of a face detector according to anexemplary embodiment of the present invention.

As shown in FIG. 4, the face detector 221 according to an exemplaryembodiment of the present invention includes a circle detector 2211 andan image analyzer 2212, and operates in connection with a cascadeclassifier 31 of the training unit 30. The image pyramid is input to thecircle detector 221, and the circle detector 221 detects all areashaving a radius in an image, that is, circles, by using a Hough circletransform for circle detection.

The circle detector 221 constructs a map based on the results of thecircle detection and indicates points corresponding to the centers ofall the circles in the map. Then, the circle detector 221 forms a maskedimage by using the input image and the map, that is, a circle map, andprovides the masked image to the image analyzer 2212.

The image analyzer 2212 generates a set of fragments by scanning themasked image. The set of fragments including image fragments isprocessed by the cascade classifier in the following stage.

FIG. 5 shows an operation of a classifier according to an exemplaryembodiment of the present invention.

The classifier 31 according to an exemplary embodiment of the present isa cascade classifier. The cascade classifier 31 includes a plurality ofclassifiers, and particularly includes homogeneous classifiers. Theimage analyzer 2212 of the image detector 221 calls for all theclassifiers to determine the type of an image fragment as one of a facetype and a non-face type.

Each classifier of the cascade classifier 31 operates with descriptorvectors which are constructed by image fragments.

In the first stage of cascade, descriptor vectors for fragments to beinput are formed and the descriptor vectors have a short length (501,502). The first classifier processes the descriptor vectors (S503).

When the process on the descriptor vectors having a short length hasbeen completed, the fragments which are classified into a non-face typeby the first classifier are eliminated from the set of fragments. Thefirst classification process performed by the first classifier uses avector of a relatively short length, and thereby the speed of the firstclassification process is fast. The fragment elimination process onnegative samples (image fragments classified into a non-face type) isindicated as “NF (non-face)” in FIG. 5. Image fragments regarded asnegative samples are included in the set of rejected samples (506).

When a classifier (the first classifier) classifies an image fragmentinto a face type, the image fragment is provided to the next classifierof the cascade. This process corresponds to the arrow indicated as “F”(face) in FIG. 5.

When four stages of the cascade classifier are completed (504, 505), theimage fragments not included in the set of the fragments that areprocessed as a rejected sample and are rejected by any classifier of thecascade classifier are passed to the stage for an additional processsuch as processing or output processing by an additional classifier inthe cascade classifier, e.g., highlighting the instances of faces in anoutput image (507).

The later classifiers of the cascade classifier perform elimination forthe negative samples classified as the non-face type, but requireadditional computation as compared with the first classifier. All theclassifiers after the first classifier in the cascade classifier operatewith vectors having progressively larger lengths, and thereby theprocessing speed is slower than that of the first stage performed by thefirst classifier.

A set of image fragments generally formed by an image processor includesa large number of non-face-type fragments and few face-type fragments.Most of the non-face-type fragments are eliminated in the first stage inthe cascade classifier, and only a small number of fragments areprocessed through all the following stages. As above, a large number ofnon-face-type fragments are eliminated in the first stage and only asmall number of fragments are processed in computationally expensivestages. Therefore, this scheme of processing provides high computationalspeed for the whole cascade.

The data input to the cascade classifier are vectors calculated by usinga processed fragment. First, a fragment is scaled with a ratiocorresponding to the position of a classifier in the cascade classifier,and then a local binary pattern (LBP) descriptor is calculated. Thefirst stages of the cascade use a large scaling factor or scaling ratioand use descriptor vectors of a smaller length, which provides highcomputational speed of classifiers in these stages.

A LBP construction procedure includes pair-wise intensity comparisonbetween a pixel and 8 neighboring pixels. The results of the comparisonare coded with a binary 8-element vector, and a value of each element ofthe vector is “0” or “1” based on each of the results. These binaryvectors have a binary notation of a decimal number from the range [0,255]. That is, the results of the LBP are a decimal number calculated ona related binary vector. Each binary vector has an 8-bit binary notationand corresponds to one among the decimal numbers from 0 to 255. FIG. 6shows this LBP procedure.

Referring to FIG. 6, intensity comparison between a predetermined pixelhaving an instance value of “78” and 8 neighboring pixels is performed.

When an instance value of a neighboring pixel is smaller than that thatof the predetermine pixel, “0” is substituted for the instance value ofthe neighboring pixel. When an instance value of a neighboring pixel islarger than that of the predetermine pixel, “1” is substituted for theinstance value of the neighboring pixel.

Through the above process, the substituted values of the pixels arearranged based on the values of the upper left pixel among the pixels(00111010), and the resulting value “58” of the LBP operation iscalculated by the decimal arithmetic operation on the arranged pixelvalues.

The LBP operation is applied to all the pixels of an image fragment, andthe descriptor vector is a vector of all LBP resulting values. Thedescriptor vector is a matrix of an H×W size, and elements of the matrixare integers from 0 to 255.

Each classifier included in the cascade may be an artificial neuralnetwork (NN) with SNOW architecture. A layer of the artificial neuralnetwork has a feed-forward structure, and is fully connected to thenetwork. The size of input layer is based on the length of thedescriptor vector and is calculated according to the following Equation1.

N=256×W×H  (Equation 1)

Here, W represents the width of the descriptor vector and H representsthe height of the descriptor vector.

The output layer consists of two neurons. One of them codes the facetypes and the other codes the non-face types.

The input vector input to the neural network is binary, and the numberof elements corresponding to “1” is equal to W×H. The size of the inputvector is large, but most of the elements of the input vector are “0”.Thus, when calculating the output of the neural network, the number W×Hof the elements of the input vector are used. Accordingly, it ispossible to improve the processing speed of the neural network.

An input vector is constructed by calculating positions of unit elementsand setting all the elements except for the unit element as “0”, and thepositions of the unit elements are calculated as follows.

Ind=256×(y×W+x)+D(x,y)  (Equation 2)

Here, x and y represent coordinates of an element of a descriptor, and Wrepresents the width of the descriptor.

An activation function ƒ(u) is a sigmoid function, and may be shown inthe following.

$\begin{matrix}{{f(u)} = \frac{1}{1 + ^{- u}}} & \left( {{Equation}\mspace{14mu} 3} \right)\end{matrix}$

The decision on the input vector classification is made based on theresults of comparison of the output neuron values of the NN.

In an exemplary embodiment of the present invention, a procedure fordetermining a type of an input vector is performed as follows.

First, linear combination of the input vector and neuron weight valuesis performed.

$\begin{matrix}{{u_{i} = {\sum\limits_{j = 1}^{N}{w_{ij}z_{j}}}},{i = 1},2} & \left( {{Equation}\mspace{14mu} 4} \right)\end{matrix}$

In Equation 4, w represents a neuron weight value, z represents an inputvector, and i and j represent an index.

Next, a non-linear function will be calculated.

$\begin{matrix}{{g\left( {u_{1},u_{2}} \right)} = \frac{1}{1 + ^{- {({u_{1} - u_{2}})}}}} & \left( {{Equation}\mspace{14mu} 5} \right)\end{matrix}$

Here, g(u₁,u₂) represents a value of the non-linear function.

When a value of the non-linear function is larger than that of athreshold value thr₁, the corresponding input vector is determined as aface type. When a value of the non-linear function is not larger thanthat of the threshold value thr₁, the corresponding input vector isdetermined as a non-face type. Here, the threshold value may becalculated as follows.

$\begin{matrix}{{thr}_{1} = {- {\log \left( {\frac{1}{thr} - 1} \right)}}} & \left( {{Equation}\mspace{14mu} 6} \right)\end{matrix}$

The comparison of the non-linear function value for each input vectorand the threshold value may be replaced with the calculation of anexpression u₁-u₂ and the comparison of its value and the threshold valuethr₁.

The difference (u₁-u₂) between two linear combinations may be calculatedas follows.

$\begin{matrix}{{u_{1} - u_{2}} = {\sum\limits_{j = 1}^{N}{\left( {w_{1\; j} - w_{2\; j}} \right)z_{j}}}} & \left( {{Equation}\mspace{14mu} 7} \right)\end{matrix}$

The neural network may be replaced with new one that has the same inputlayer, but only one neuron in an output layer. Weight values of the oneneuron are equal to differences between respective weights of neurons ofthe previous neural network, and the new neural network requires halfthe computations.

The cascade according to an exemplary embodiment of the presentinvention may include four neural networks that have the above-describedstructure, but is not restricted to thereto.

A fragment input to a classifier includes 24×24 pixels.

At the first stage, a neural network processing descriptors of 3×3pixels operates. Next, the descriptor of 4×3 pixels operates, then thedescriptor of 6×6 pixels, and finally the descriptor of 12×12 pixelsoperates.

The structure of the neural network according to an exemplary embodimentof the present invention may be selected based on the experimentalresults. In order to obtain the experimental results, a predeterminedtraining algorithm, which may be one of well-known algorithm, is usedfor calculating weight values.

A training stage is performed in an exemplary embodiment of the presentinvention. The training stage may be performed by the training unit 30,and includes the two sets of training and test. Each set includesface-type of fragments and non-face-type of fragments, neuron weightvalues are calculated in the training set, and the efficiency of the NNis verified on samples from the test set.

The training set includes the following steps.

1) Initial training procedure

2) One or a plurality of steps of a bootstrap procedure

First, the NN, operating at the first stage, is trained.

The NN is launched on training samples, and all samples rejected by theNN are eliminated from the training samples. The same procedure isperformed on the test set.

The updated sets are used for training the NN operating at the secondstage. Similar to the first stage, the NN operating at the second stageprocesses the samples of the training set and the test set, and samplesclassified into a non-face type are removed from the samples. Theseprocedures are repeated for all the NNs included in the cascade.

A training process of a single NN is controlled by using the test set.In an exemplary embodiment of the present invention, the number ofnon-face type samples, on which a NN fails, is fixed. After everytraining procedure, the number of samples that are mistakenly classifiedinto a face type while an NN performs a training process is counted. Thetraining procedure may be continued until the number of samplesdecreases.

As stated above, in the exemplary embodiment of the present invention,until the number of samples that are classified into a face type byerrors reaches the minimum value, the training procedure may becontinued. Then, an NN satisfying the minimum values is determined.

The bootstrap procedure is performed as follows.

The cascade of classifiers is formed on the images that include no face.All image fragments on which the cascade makes mistakes are added to thetraining set. That is, the image fragments mistakenly determined as anon-face type by the cascade are added to the training set. All the NNsof the cascade are retrained by using the updated training set.

Next, a method for detecting a face according to an exemplary embodimentof the present invention will be described based on the above.

FIG. 7 shows a flowchart of a method for detecting a face according toan exemplary embodiment of the present invention.

Image pyramids that are scaled at the first stage are obtained (S710),and a face is detected from each image of the image pyramids through thefollowing steps.

Circles corresponding to the regions of a radius are detected from thescaled images by using a Hough transform (S720). By scanning the regionscorresponding to the circles, a set of image fragments forclassification is formed (S730). The set of image fragments is input tothe cascade classifier 31 shown in FIG. 5.

The cascade classifier 31 is a cascade of neural networks, and includesneural networks (referred to as “a first neural network”) that have highcomputational speed and operate at the first stage). These first neuralnetworks use vectors of a short length and perform rough processing onan input image. Most of image fragments that do not represent a face areremoved while image fragments representing a face are preserved forfurther processing.

In the next stage of the cascade, more complicated and slower neuralnetworks (referred to as “a second neural network”) operate. Thesesecond neural networks process high dimensional vectors, and moreprecisely distinguish image fragments including a face and imagefragments not including a face. Through the first and second neuralnetworks, image fragments are classified into a face type or a non-facetype (S740).

As stated above, the face detector 221 performs classificationprocessing on all images of the image pyramid through the cascade of theneural networks and provides the results of the classificationprocessing to the face integrator 222 (S750).

Then, the face integrator 222 determines whether the classificationprocessing on all image of the image pyramid has been completed (S750).When the classification processing has been completed, the faceintegrator 222 calculates coordinates of faces detected from the imagesbased on the results of the classification processing by using aclusterization and heuristics algorithm (S760). After this, the methodfor detecting a face finishes.

According to the exemplary embodiments of the present invention, stagesfor performing algorithms for face detection are constructed in a typeof a cascade, and process both image information of the same type andimage information of different types so that high efficiency of thealgorithm is provided. The cascade structure has a characteristic offlexibility, and the number of stages included in the cascade isvariable. Through the stages of the cascade, it is possible to detect aface with high accuracy and high speed.

The method and apparatus for detecting a face according to the exemplaryembodiments of the present invention may be applied to a biometricsystem for identifying a face of a person from an image, and may furtherbe embodied by being applied to special equipment such as a signalprocessor.

An exemplary embodiment of the present invention may not only beembodied through the above-described apparatus and/or method, but mayalso be embodied through a program that executes a functioncorresponding to a configuration of an exemplary embodiment of thepresent invention and through a recording medium on which the program isrecorded.

While this invention has been described in connection with what ispresently considered to be practical exemplary embodiments, it is to beunderstood that the invention is not limited to the disclosedembodiments, but, on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A method for detecting a face, comprising: detecting a circle from aninput image by using a Hough transform; forming image fragments of thesame size in a region of the detected circle; generating input vectorsfor each of the image fragments by using each classifier of a cascadethat includes homogeneous classifiers; and when an input vectorgenerated by the image fragments is classified into a face type by theclassifier of the cascade, determining that a face is detected from theinput image.
 2. The method of claim 1, wherein the determining furtherincludes classifying input vectors into a face type and a non-face type,and the detecting of a circle further includes forming an image pyramidincluding images that are copies of images scaled down by apredetermined ratio.
 3. The method of claim 2, wherein the classifyingof input vectors further includes training the homogeneous classifiersbased on a database including a training set.
 4. The method of claim 1,wherein the input vector is calculated based on a local binary pattern(LBP) descriptor and represents the input image.
 5. The method of claim1, wherein the classifier is provided with an input vector of a largersize in comparison with a previous classifier, and the input vector isformed for an image fragment corresponding to an image scaled down by apredetermined ratio, and the predetermined ratio gradationally isreduced.
 6. The method of claim 1, wherein the classifier of the cascadeoperates based on an artificial neural network of a SNOW structure. 7.An apparatus for detecting a face, comprising: a cascade classifier thatis formed in a type of a cascade, includes homogeneous classifiers, andclassifies input vectors into a face type and a non-face type based oninput images; a circle detector that detects a circle from the inputimage by using a Hough transform; and an image analyzer that forms imagefragments of the same size within a region of the detected circle,wherein the image analyzer generates input vectors for the imagefragments by using the classifiers of the cascade classifier, anddetermines that a face is detected from the input image when the inputvector is classified into the face type by all the classifiers.
 8. Theapparatus of claim 7, further comprising an image pyramid constructorfor including images that are copies of images scaled by a predeterminedratio.
 9. The apparatus of claim 7, further comprising a training unitfor training the homogeneous classifiers based on a database including atraining set.
 10. The apparatus of claim 7, wherein the input vector iscalculated based on a local binary pattern (LBP) descriptor andrepresents the input image.
 11. The apparatus of claim 7, wherein theclassifier is provided with an input vector of a larger size incomparison with a previous classifier, and the input vector is formedfor an image fragment corresponding to an image scaled down by apredetermined ratio, and the predetermined ratio gradationally isreduced.
 12. The apparatus of claim 7, wherein the classifier of thecascade operates based on an artificial neural network of a SNOWstructure.